Ripk2 dictates blood insulin and glucose responses to cancer drug and microbial xenobiotics
Bibliographic record
Abstract
Xenobiotics, including therapeutic drugs, and components of microbes can promote inflammation that contributes to insulin resistance and dysglycemia. Tyrosine kinase inhibitors (TKIs) used in cancer have been considered as a therapy in diabetes and its complications. It is not clear which molecular target of TKIs impact blood glucose or insulin during obesity. One objective here was to define the role of the innate immune adapter protein, Ripk2 in TKI‐mediated changes in blood glucose and insulin during obesity. We tested gefitinib, which inhibits Ripk2 and imatinib, which does not inhibit Ripk2 as TKI interventions in diet‐induced obese mice. We also tested whether Ripk2 and these TKIs prevented the insulin sensitizing properties of bacterial components that we already know lower glucose and insulin resistance via Nod2‐Ripk2. We previously found specific bacterial cell components that lower blood glucose and insulin resistance. Now we found that crude bacterial extracts from the upper gut also lower blood glucose. Hence, the other objective was to determine if Ripk2 propagated insulin sensitizing properties of extracts from commensal bacteria. We found gefitinib lowered blood glucose during a glucose tolerance test (GTT) in WT, Nod1‐null, Nod2‐null and Ripk2‐null mice that were obese. Gefitinib lowered glucose stimulated blood insulin levels only in obese Ripk2‐null mice. Hence, deletion of Ripk2 in mice promoted the insulin sensitizing potential of gefitinib. Gefitinib, but not imatinib, prevented improvements in glucose control caused by a bacterial postbiotic that improves insulin sensitivity. Further, Ripk2 was required for improvements in glucose control when mice were injected bacterial extracts from the upper gut of obese or lean mice. We conclude that multiple TKIs, including gefitinib and imatinib lower blood glucose in obese mice, independent of Ripk2. However, Ripk2 participated in TKI‐induced lowering of blood insulin in response to an oral glucose load. The effects of TKIs on Ripk2‐mediated metabolism should be measured if this drug class is considered in treatment of diabetes or its complications. Also, the role of Ripk2 in propagating bacterial‐induced changes in blood glucose and insulin warrants further investigation. Support or Funding Information Supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) discovery grant. BD was supported by Ontario Graduate Scholarships. JC was supported by a Frederick Banting and Charles Best Canada Graduate Scholarship. KF was supported by a NSERC fellowship. JS holds a Canada Research Chair in Metabolic Inflammation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".